KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation
This work addresses commonsense reasoning in NLP, but it is incremental as it builds on existing pre-trained models and focuses on a specific competition task.
The paper tackled commonsense validation and explanation by proposing a novel evidence-searching approach with large-scale pre-trained models, achieving second place in a subtask based on human evaluation scores.
This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.